Exploratory Factor Analysis Task

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An Exploratory Factor Analysis Task is a factor analysis task that is an exploratory analysis to uncover the underlying structure of a structured dataset.



  • http://en.wikipedia.org/wiki/Exploratory_factor_analysis
    • In multivariate statistics, exploratory factor analysis (EFA) is a statistical method used to uncover the underlying structure of a relatively large set of variables. EFA is a technique within factor analysis whose overarching goal is to identify the underlying relationships between measured variables.[1] It is commonly used by researchers when developing a scale (a scale is a collection of questions used to measure a particular research topic) and serves to identify a set of latent constructs underlying a battery of measured variables.[2] It should be used when the researcher has no a priori hypothesis about factors or patterns of measured variables.[3] Measured variables are any one of several attributes of people that may be observed and measured. An example of a measured variable would be the physical height of a human being. Researchers must carefully consider the number of measured variables to include in the analysis.[2] EFA procedures are more accurate when each factor is represented by multiple measured variables in the analysis. There should be at least 3 to 5 measured variables per factor.[4]
  1. Norris, Megan; Lecavalier, Luc (17 July 2009). "Evaluating the Use of Exploratory Factor Analysis in Developmental Disability Psychological Research". Journal of Autism and Developmental Disorders 40 (1): 8–20. doi:10.1007/s10803-009-0816-2. 
  2. 2.0 2.1 Fabrigar, Leandre R.; Wegener, Duane T., MacCallum, Robert C., Strahan, Erin J. (1 January 1999). "Evaluating the use of exploratory factor analysis in psychological research.". Psychological Methods 4 (3): 272–299. doi:10.1037/1082-989X.4.3.272. 
  3. Finch, J. F., & West, S. G. (1997). “The investigation of personality structure: Statistical models". Journal of Research in Personality, 31 (4), 439-485.
  4. Maccallum, R. C. (1990). “The need for alternative measures of fit in covariance structure modeling". Multivariate Behavioral Research, 25(2), 157-162.